{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### experiment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "# Add parent directory to Python path for importing custom modules\n",
    "sys.path.append(os.path.abspath('../..'))\n",
    "\n",
    "from utils.io.pd_process import construct_logical_response_matrix, construct_response_matrix\n",
    "\n",
    "M_1 = np.load('./[0, 1]/transition_matrix_q0.npy', allow_pickle=True).item()['transition_matrix']\n",
    "M_2 = np.load('./[0, 1]/transition_matrix_q0_q1.npy', allow_pickle=True).item()['transition_matrix']\n",
    "\n",
    "M_0011 = np.array([[M_2[0, 0], M_2[0, -1]],\n",
    "                  [M_2[-1, 0], M_2[-1, -1]]])\n",
    "\n",
    "M_0110 = np.array([[M_2[1, 1], M_2[1, 2]],\n",
    "                  [M_2[-2, 1], M_2[-2, 2]]])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check original coding performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dict_to_array(counts: dict, mapping: dict, normalized: bool=False) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Convert a dictionary of counts to a numpy array.\n",
    "    \n",
    "    Args:\n",
    "        counts: Dictionary of counts\n",
    "        mapping: Dictionary of mapping\n",
    "        normalized: Whether to normalize the counts\n",
    "\n",
    "    Returns:\n",
    "        Numpy array of counts\n",
    "    \"\"\"\n",
    "    logical_nqubits = len(mapping) // 2\n",
    "    res = np.zeros(2**logical_nqubits)\n",
    "    \n",
    "    for measured_state in mapping.keys():\n",
    "        res[int(mapping[measured_state], 2)] = counts[measured_state]\n",
    "    \n",
    "    if normalized:\n",
    "        res = res / np.sum(res)\n",
    "    return res\n",
    "\n",
    "\n",
    "test_circ_h1 = np.load('./[0, 1]/ghz_shots100000_q0.npy', allow_pickle=True).item()['counts']\n",
    "test_circ_h0011 = np.load('./[0, 1]/ghz_shots100000.npy', allow_pickle=True).item()['counts']\n",
    "test_circ_h0110 = np.load('./[0, 1]/ghz_shots100000_qed.npy', allow_pickle=True).item()['counts']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## quantify the distance with the original data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.core.quantify import TVD\n",
    "\n",
    "tvd_origin = {\n",
    "    100000: TVD(dict_to_array(test_circ_h1, {'0': '0', '1': '1'}), np.ones(2)/2)\n",
    "}\n",
    "\n",
    "tvd_00and11 = {\n",
    "    100000: TVD(dict_to_array(test_circ_h0011, {'00': '0', '11': '1'}, normalized=True), np.ones(2)/2)\n",
    "}\n",
    "\n",
    "tvd_01and10 = {\n",
    "    100000: TVD(dict_to_array(test_circ_h0110, {'01': '0', '10': '1'}, normalized=True), np.ones(2)/2)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## show the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x975 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Variation Distance (TVD) values:\n",
      "1-qubit Hadamard: {'100000': np.float64(0.007589999999999986)}\n",
      "00,11 QED: {'100000': np.float64(0.014174344436569813)}\n",
      "01,10 QED: {'100000': np.float64(0.0033951068675176777)}\n",
      "\n",
      "## Circuit Performance Improvements\n",
      "\n",
      "Percentage improvement in TVD compared to 1-qubit Hadamard circuit:\n",
      "\n",
      "### 00/11 Circuit vs 1-qubit Hadamard\n",
      "\n",
      "| Shots | Improvement (%) |\n",
      "|--------|----------------|\n",
      "| 100000 | -86.75% |\n",
      "\n",
      "### 01/10 Circuit vs 1-qubit Hadamard\n",
      "\n",
      "| Shots | Improvement (%) |\n",
      "|--------|----------------|\n",
      "| 100000 | 55.27% |\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import matplotlib as mpl\n",
    "\n",
    "# Set up the plotting style to match high-quality scientific journals\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'Arial',\n",
    "    'font.size': 10,\n",
    "    'axes.linewidth': 1.0,\n",
    "    'axes.labelsize': 12,\n",
    "    'xtick.major.width': 1.0,\n",
    "    'ytick.major.width': 1.0,\n",
    "    'xtick.labelsize': 10,\n",
    "    'ytick.labelsize': 10,\n",
    "    'legend.fontsize': 10,\n",
    "    'legend.frameon': False,\n",
    "    'figure.dpi': 150,\n",
    "    'savefig.dpi': 300,\n",
    "    'savefig.bbox': 'tight'\n",
    "})\n",
    "\n",
    "# Convert the dictionaries to more convenient format for plotting\n",
    "shots = [100000]\n",
    "tvd_values = {\n",
    "    '1-qubit Hadamard': [tvd_origin[100000]],\n",
    "    '00,11 QED': [tvd_00and11[100000]],\n",
    "    '01,10 QED': [tvd_01and10[100000]]\n",
    "}\n",
    "\n",
    "# Create the figure and axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# Define a color palette that matches scientific publications\n",
    "colors = ['#0072B2', '#D55E00', '#009E73']\n",
    "markers = ['o', 's', '^']\n",
    "\n",
    "# Plot the data with error bars\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    ax.plot(shots, tvd_data, marker=markers[i], markersize=8, \n",
    "            color=colors[i], label=circuit_type, linewidth=2)\n",
    "    \n",
    "    # Add points with a slight offset for better visibility\n",
    "    for j, (shot, tvd) in enumerate(zip(shots, tvd_data)):\n",
    "        ax.scatter(shot, tvd, s=80, facecolors='white', \n",
    "                  edgecolors=colors[i], linewidth=1.5, zorder=3)\n",
    "\n",
    "# Set up the figure for bar plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6.5))\n",
    "\n",
    "# Set width of bars and positions\n",
    "bar_width = 0.25\n",
    "positions = np.arange(len(shots))\n",
    "\n",
    "# Create grouped bar chart\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    offset = (i - 1) * bar_width\n",
    "    bars = ax.bar(positions + offset, tvd_data, bar_width, \n",
    "                 color=colors[i], label=circuit_type, edgecolor='black', linewidth=1)\n",
    "    \n",
    "    # # Add value labels on top of bars\n",
    "    # for j, bar in enumerate(bars):\n",
    "    #     height = bar.get_height()\n",
    "    #     ax.text(bar.get_x() + bar.get_width()/2., height + 0.005,\n",
    "    #             f'{height:.3f}', ha='center', va='bottom', fontsize=9)\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Number of Shots', fontweight='bold')\n",
    "ax.set_ylabel('Total Variation Distance (TVD)', fontweight='bold')\n",
    "ax.set_title('TVD vs. Number of Shots', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Set x-ticks to show shot counts\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels([str(shot) for shot in shots])\n",
    "\n",
    "# Add a horizontal line at TVD = 0 for reference\n",
    "ax.axhline(y=0, color='gray', linestyle='--', alpha=0.5, linewidth=1)\n",
    "\n",
    "# Customize the grid (only horizontal grid lines for bar charts)\n",
    "ax.yaxis.grid(True, linestyle='--', alpha=0.7)\n",
    "ax.set_ylim(0, 0.045)\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# Add minor ticks for y-axis\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "\n",
    "# Customize the legend\n",
    "ax.legend(loc='upper right', frameon=True, fancybox=False, edgecolor='black', framealpha=0.8)\n",
    "\n",
    "# Add annotations to explain the meaning of TVD\n",
    "ax.text(0.02, 0.02, 'Lower TVD = Better fidelity to ideal distribution', \n",
    "        transform=ax.transAxes, fontsize=9, style='italic')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print the numerical values for reference\n",
    "print(\"Total Variation Distance (TVD) values:\")\n",
    "for circuit_type, tvd_data in tvd_values.items():\n",
    "    print(f\"{circuit_type}: {dict(zip([str(s) for s in shots], tvd_data))}\")\n",
    "\n",
    "# Calculate improvement percentages\n",
    "improvements = {\n",
    "    '00and11 vs 1-qubit': {},\n",
    "    '01and10 vs 1-qubit': {}\n",
    "}\n",
    "\n",
    "# Calculate improvement for each shot count\n",
    "for i, shots in enumerate([100000]):\n",
    "    # 00and11 vs 1-qubit improvement\n",
    "    hadamard_tvd = tvd_values['1-qubit Hadamard'][i]\n",
    "    zerozero_tvd = tvd_values['00,11 QED'][i]\n",
    "    improvement = ((hadamard_tvd - zerozero_tvd) / hadamard_tvd) * 100\n",
    "    improvements['00and11 vs 1-qubit'][shots] = improvement\n",
    "    \n",
    "    # 01and10 vs 1-qubit improvement\n",
    "    zeroone_tvd = tvd_values['01,10 QED'][i]\n",
    "    improvement = ((hadamard_tvd - zeroone_tvd) / hadamard_tvd) * 100\n",
    "    improvements['01and10 vs 1-qubit'][shots] = improvement\n",
    "\n",
    "# Print markdown formatted results\n",
    "print(\"\\n## Circuit Performance Improvements\")\n",
    "print(\"\\nPercentage improvement in TVD compared to 1-qubit Hadamard circuit:\\n\")\n",
    "\n",
    "print(\"### 00/11 Circuit vs 1-qubit Hadamard\")\n",
    "print(\"\\n| Shots | Improvement (%) |\")\n",
    "print(\"|--------|----------------|\")\n",
    "for shots, imp in improvements['00and11 vs 1-qubit'].items():\n",
    "    print(f\"| {shots} | {imp:.2f}% |\")\n",
    "\n",
    "print(\"\\n### 01/10 Circuit vs 1-qubit Hadamard\") \n",
    "print(\"\\n| Shots | Improvement (%) |\")\n",
    "print(\"|--------|----------------|\")\n",
    "for shots, imp in improvements['01and10 vs 1-qubit'].items():\n",
    "    print(f\"| {shots} | {imp:.2f}% |\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# apply mitigation method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.core.algs import matrix_inversion_mitigation, IBU\n",
    "\n",
    "\n",
    "# apply matrix inversion mitigation\n",
    "MI_p = {\n",
    "    '1-qubit_hadamard': {\n",
    "        100000: matrix_inversion_mitigation(dict_to_array(test_circ_h1, {'0': '0', '1': '1'}), M_1),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        100000: matrix_inversion_mitigation(dict_to_array(test_circ_h0011, {'00': '0', '11': '1'}, normalized=False), M_0011),\n",
    "    },\n",
    "    \n",
    "    '01and10': {\n",
    "        100000: matrix_inversion_mitigation(dict_to_array(test_circ_h0110, {'01': '0', '10': '1'}, normalized=False), M_0110)\n",
    "    }\n",
    "}\n",
    "\n",
    "# apply IBU\n",
    "IBU_p = {\n",
    "    '1-qubit_hadamard': {\n",
    "        100000: IBU(dict_to_array(test_circ_h1, {'0': '0', '1': '1'}), np.ones(2)/2, M_1, 10),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        100000: IBU(dict_to_array(test_circ_h0011, {'00': '0', '11': '1'}, normalized=True), np.ones(2)/2, M_0011, 10),\n",
    "    },\n",
    "\n",
    "    '01and10': {\n",
    "        100000: IBU(dict_to_array(test_circ_h0110, {'01': '0', '10': '1'}, normalized=True), np.ones(2)/2, M_0110, 10),\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Input distributions were normalized. Original sums: p=0.993506, q=1.000000\n",
      "Warning: Input distributions were normalized. Original sums: p=0.989579, q=1.000000\n"
     ]
    }
   ],
   "source": [
    "tvd_MI = {\n",
    "    '1-qubit_hadamard': {\n",
    "        100000: TVD(MI_p['1-qubit_hadamard'][100000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        100000: TVD(MI_p['00and11'][100000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '01and10': {\n",
    "        100000: TVD(MI_p['01and10'][100000], np.ones(2)/2),\n",
    "    }\n",
    "}\n",
    "\n",
    "tvd_IBU = {\n",
    "    '1-qubit_hadamard': {\n",
    "        100000: TVD(IBU_p['1-qubit_hadamard'][100000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        100000: TVD(IBU_p['00and11'][100000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '01and10': {\n",
    "        100000: TVD(IBU_p['01and10'][100000], np.ones(2)/2),\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## show the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x975 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Variation Distance (TVD) values:\n",
      "1-qubit Hadamard+Matrix Inversion: {'100000': np.float64(0.0005761079790954915)}\n",
      "00,11 QED+Matrix Inversion: {'100000': np.float64(0.002295025974001663)}\n",
      "01,10 QED+Matrix Inversion: {'100000': np.float64(0.005981616650836491)}\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import matplotlib as mpl\n",
    "\n",
    "# Set up the plotting style to match high-quality scientific journals\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'Arial',\n",
    "    'font.size': 10,\n",
    "    'axes.linewidth': 1.0,\n",
    "    'axes.labelsize': 12,\n",
    "    'xtick.major.width': 1.0,\n",
    "    'ytick.major.width': 1.0,\n",
    "    'xtick.labelsize': 10,\n",
    "    'ytick.labelsize': 10,\n",
    "    'legend.fontsize': 10,\n",
    "    'legend.frameon': False,\n",
    "    'figure.dpi': 150,\n",
    "    'savefig.dpi': 300,\n",
    "    'savefig.bbox': 'tight'\n",
    "})\n",
    "\n",
    "# Convert the dictionaries to more convenient format for plotting\n",
    "shots = [100000]\n",
    "# tvd_values = {\n",
    "#     '1-qubit Hadamard': [tvd_MI['1-qubit_hadamard'][1000], tvd_MI['1-qubit_hadamard'][5000], tvd_MI['1-qubit_hadamard'][10000]],\n",
    "#     '00,11 QED': [tvd_MI['00and11'][1000], tvd_MI['00and11'][5000], tvd_MI['00and11'][10000]],\n",
    "#     '01,10 QED': [tvd_MI['01and10'][1000], tvd_MI['01and10'][5000], tvd_MI['01and10'][10000]]\n",
    "# }\n",
    "\n",
    "tvd_values = {\n",
    "    '1-qubit Hadamard+Matrix Inversion': [tvd_MI['1-qubit_hadamard'][100000]],\n",
    "    '00,11 QED+Matrix Inversion': [tvd_MI['00and11'][100000]],\n",
    "    '01,10 QED+Matrix Inversion': [tvd_MI['01and10'][100000]]\n",
    "}\n",
    "\n",
    "# Create the figure and axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# Define a color palette that matches scientific publications\n",
    "colors = ['#0072B2', '#D55E00', '#009E73']\n",
    "markers = ['o', 's', '^']\n",
    "\n",
    "# Plot the data with error bars\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    ax.plot(shots, tvd_data, marker=markers[i], markersize=8, \n",
    "            color=colors[i], label=circuit_type, linewidth=2)\n",
    "    \n",
    "    # Add points with a slight offset for better visibility\n",
    "    for j, (shot, tvd) in enumerate(zip(shots, tvd_data)):\n",
    "        ax.scatter(shot, tvd, s=80, facecolors='white', \n",
    "                  edgecolors=colors[i], linewidth=1.5, zorder=3)\n",
    "\n",
    "# Set up the figure for bar plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6.5))\n",
    "\n",
    "# Set width of bars and positions\n",
    "bar_width = 0.25\n",
    "positions = np.arange(len(shots))\n",
    "\n",
    "# Create grouped bar chart\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    offset = (i - 1) * bar_width\n",
    "    bars = ax.bar(positions + offset, tvd_data, bar_width, \n",
    "                 color=colors[i], label=circuit_type, edgecolor='black', linewidth=1)\n",
    "    \n",
    "    # # Add value labels on top of bars\n",
    "    # for j, bar in enumerate(bars):\n",
    "    #     height = bar.get_height()\n",
    "    #     ax.text(bar.get_x() + bar.get_width()/2., height + 0.005,\n",
    "    #             f'{height:.3f}', ha='center', va='bottom', fontsize=9)\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Number of Shots', fontweight='bold')\n",
    "ax.set_ylabel('Total Variation Distance (TVD)', fontweight='bold')\n",
    "ax.set_title('TVD vs. Number of Shots (Matrix Inversion )', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Set x-ticks to show shot counts\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels([str(shot) for shot in shots])\n",
    "\n",
    "# Add a horizontal line at TVD = 0 for reference\n",
    "ax.axhline(y=0, color='gray', linestyle='--', alpha=0.5, linewidth=1)\n",
    "\n",
    "# Customize the grid (only horizontal grid lines for bar charts)\n",
    "ax.yaxis.grid(True, linestyle='--', alpha=0.7)\n",
    "ax.set_ylim(0, 0.045)\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# Add minor ticks for y-axis\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "\n",
    "# Customize the legend\n",
    "ax.legend(loc='upper right', frameon=True, fancybox=False, edgecolor='black', framealpha=0.8)\n",
    "\n",
    "# Add annotations to explain the meaning of TVD\n",
    "ax.text(0.02, 0.02, 'Lower TVD = Better fidelity to ideal distribution', \n",
    "        transform=ax.transAxes, fontsize=9, style='italic')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print the numerical values for reference\n",
    "print(\"Total Variation Distance (TVD) values:\")\n",
    "for circuit_type, tvd_data in tvd_values.items():\n",
    "    print(f\"{circuit_type}: {dict(zip([str(s) for s in shots], tvd_data))}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analyse fluctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Noise 0.01, Shots 100: 100%|██████████| 100/100 [00:00<00:00, 33267.01it/s]\n",
      "Noise 0.01, Shots 500: 100%|██████████| 100/100 [00:00<00:00, 33325.15it/s]\n",
      "Noise 0.01, Shots 1000: 100%|██████████| 100/100 [00:00<00:00, 38189.06it/s]\n",
      "Noise 0.01, Shots 5000: 100%|██████████| 100/100 [00:00<00:00, 43482.31it/s]\n",
      "Noise 0.01, Shots 10000: 100%|██████████| 100/100 [00:00<00:00, 33272.28it/s]\n",
      "Noise 0.01, Shots 50000: 100%|██████████| 100/100 [00:00<00:00, 39595.05it/s]\n",
      "Noise 0.05, Shots 100: 100%|██████████| 100/100 [00:00<00:00, 39888.77it/s]\n",
      "Noise 0.05, Shots 500: 100%|██████████| 100/100 [00:00<00:00, 40469.93it/s]\n",
      "Noise 0.05, Shots 1000: 100%|██████████| 100/100 [00:00<00:00, 35980.99it/s]\n",
      "Noise 0.05, Shots 5000: 100%|██████████| 100/100 [00:00<00:00, 33349.00it/s]\n",
      "Noise 0.05, Shots 10000: 100%|██████████| 100/100 [00:00<00:00, 39531.61it/s]\n",
      "Noise 0.05, Shots 50000: 100%|██████████| 100/100 [00:00<00:00, 37691.45it/s]\n",
      "Noise 0.1, Shots 100: 100%|██████████| 100/100 [00:00<00:00, 65751.75it/s]\n",
      "Noise 0.1, Shots 500: 100%|██████████| 100/100 [00:00<00:00, 37519.49it/s]\n",
      "Noise 0.1, Shots 1000: 100%|██████████| 100/100 [00:00<00:00, 49849.11it/s]\n",
      "Noise 0.1, Shots 5000: 100%|██████████| 100/100 [00:00<00:00, 39602.53it/s]\n",
      "Noise 0.1, Shots 10000: 100%|██████████| 100/100 [00:00<00:00, 49979.79it/s]\n",
      "Noise 0.1, Shots 50000: 100%|██████████| 100/100 [00:00<00:00, 49973.84it/s]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2250x1462.5 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Total Variation Distance (TVD) values:\n",
      "\n",
      "Calibration Error Level: 0.01\n",
      "  100 shots: 0.139860 ± 0.061467\n",
      "  500 shots: 0.063471 ± 0.028288\n",
      "  1000 shots: 0.054398 ± 0.019811\n",
      "  5000 shots: 0.046060 ± 0.010568\n",
      "  10000 shots: 0.045102 ± 0.008068\n",
      "  50000 shots: 0.045115 ± 0.003783\n",
      "\n",
      "Calibration Error Level: 0.05\n",
      "  100 shots: 0.129225 ± 0.048606\n",
      "  500 shots: 0.073351 ± 0.030594\n",
      "  1000 shots: 0.065175 ± 0.022770\n",
      "  5000 shots: 0.058906 ± 0.010717\n",
      "  10000 shots: 0.056817 ± 0.007911\n",
      "  50000 shots: 0.057310 ± 0.003652\n",
      "\n",
      "Calibration Error Level: 0.1\n",
      "  100 shots: 0.139103 ± 0.069310\n",
      "  500 shots: 0.077500 ± 0.036500\n",
      "  1000 shots: 0.061427 ± 0.027590\n",
      "  5000 shots: 0.055226 ± 0.013677\n",
      "  10000 shots: 0.054560 ± 0.009269\n",
      "  50000 shots: 0.055223 ± 0.004701\n"
     ]
    }
   ],
   "source": [
    "# Analysis of Measurement Error Propagation in Quantum State Reconstruction\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.linalg import inv\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Set plot style for publication quality\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "sns.set_context(\"paper\", font_scale=1.2)\n",
    "\n",
    "# Define a function to create a transition matrix with some noise\n",
    "def create_transition_matrix(dim, noise_level=0.05):\n",
    "    \"\"\"\n",
    "    Create a transition matrix with controllable noise level.\n",
    "    \n",
    "    Args:\n",
    "        dim: Dimension of the square matrix\n",
    "        noise_level: Standard deviation of the Gaussian noise added to matrix elements\n",
    "    \n",
    "    Returns:\n",
    "        M: Transition matrix (should be close to stochastic)\n",
    "    \"\"\"\n",
    "    # Start with an identity matrix\n",
    "    M = np.eye(dim)\n",
    "    \n",
    "    # Add some off-diagonal elements to make it more realistic\n",
    "    for i in range(dim):\n",
    "        for j in range(dim):\n",
    "            if i != j:\n",
    "                M[i, j] = np.random.uniform(0, 0.3)\n",
    "    \n",
    "    # Normalize rows to make it row-stochastic (each row sums to 1)\n",
    "    for i in range(dim):\n",
    "        M[i, :] = M[i, :] / np.sum(M[i, :])\n",
    "    \n",
    "    # Add noise to represent calibration errors\n",
    "    M += np.random.normal(0, noise_level, size=(dim, dim))\n",
    "    \n",
    "    # Re-normalize to ensure it remains approximately stochastic\n",
    "    for i in range(dim):\n",
    "        M[i, :] = np.abs(M[i, :]) / np.sum(np.abs(M[i, :]))\n",
    "    \n",
    "    return M\n",
    "\n",
    "# Define a function to simulate measurements with statistical fluctuations\n",
    "def simulate_measurement(true_state, M, num_shots):\n",
    "    \"\"\"\n",
    "    Simulate a measurement process with statistical fluctuations.\n",
    "    \n",
    "    Args:\n",
    "        true_state: The true state vector (probabilities should sum to 1)\n",
    "        M: Transition matrix\n",
    "        num_shots: Number of measurement shots\n",
    "    \n",
    "    Returns:\n",
    "        measured_state: The measured state vector with statistical noise\n",
    "    \"\"\"\n",
    "    # Calculate the ideal measured probabilities\n",
    "    ideal_measured = M @ true_state\n",
    "    \n",
    "    # Simulate sampling from this distribution\n",
    "    counts = np.random.multinomial(num_shots, ideal_measured)\n",
    "    \n",
    "    # Convert counts to probabilities\n",
    "    measured_state = counts / num_shots\n",
    "    \n",
    "    return measured_state\n",
    "\n",
    "# Define a function to reconstruct the true state from measurements\n",
    "def reconstruct_state(measured_state, M):\n",
    "    \"\"\"\n",
    "    Reconstruct the true state from measured data using matrix inversion.\n",
    "    \n",
    "    Args:\n",
    "        measured_state: The measured state vector\n",
    "        M: Transition matrix\n",
    "    \n",
    "    Returns:\n",
    "        reconstructed_state: The reconstructed state vector\n",
    "    \"\"\"\n",
    "    try:\n",
    "        M_inv = inv(M)\n",
    "        reconstructed_state = M_inv @ measured_state\n",
    "        \n",
    "        # Handle potential negative probabilities by normalization\n",
    "        reconstructed_state = np.maximum(reconstructed_state, 0)\n",
    "        if np.sum(reconstructed_state) > 0:\n",
    "            reconstructed_state = reconstructed_state / np.sum(reconstructed_state)\n",
    "            \n",
    "        return reconstructed_state\n",
    "    except np.linalg.LinAlgError:\n",
    "        # If matrix is singular, use pseudo-inverse\n",
    "        M_inv = np.linalg.pinv(M)\n",
    "        reconstructed_state = M_inv @ measured_state\n",
    "        \n",
    "        # Handle potential negative probabilities\n",
    "        reconstructed_state = np.maximum(reconstructed_state, 0)\n",
    "        if np.sum(reconstructed_state) > 0:\n",
    "            reconstructed_state = reconstructed_state / np.sum(reconstructed_state)\n",
    "            \n",
    "        return reconstructed_state\n",
    "\n",
    "# Define a function to calculate the total variation distance\n",
    "def total_variation_distance(p, q):\n",
    "    \"\"\"\n",
    "    Calculate the total variation distance between two probability distributions.\n",
    "    \n",
    "    Args:\n",
    "        p, q: Probability distributions\n",
    "    \n",
    "    Returns:\n",
    "        TVD: Total variation distance (between 0 and 1)\n",
    "    \"\"\"\n",
    "    return 0.5 * np.sum(np.abs(p - q))\n",
    "\n",
    "# Simulation parameters\n",
    "dim = 4  # Dimension of the state space (e.g., 4 for 2-qubit system)\n",
    "shots_range = [100, 500, 1000, 5000, 10000, 50000]\n",
    "num_trials = 100  # Number of trials for each shot count\n",
    "noise_levels = [0.01, 0.05, 0.1]  # Different levels of matrix calibration error\n",
    "\n",
    "# Initialize results dictionary\n",
    "results = {noise: {shots: [] for shots in shots_range} for noise in noise_levels}\n",
    "\n",
    "# Run the simulation\n",
    "for noise_level in noise_levels:\n",
    "    # Create a transition matrix with the given noise level\n",
    "    M = create_transition_matrix(dim, noise_level)\n",
    "    \n",
    "    # Create a true state (normalized random probabilities)\n",
    "    true_state = np.random.random(dim)\n",
    "    true_state = true_state / np.sum(true_state)\n",
    "    \n",
    "    for shots in shots_range:\n",
    "        tvd_values = []\n",
    "        \n",
    "        for _ in tqdm(range(num_trials), desc=f\"Noise {noise_level}, Shots {shots}\"):\n",
    "            # Simulate measurement\n",
    "            measured_state = simulate_measurement(true_state, M, shots)\n",
    "            \n",
    "            # Reconstruct the state\n",
    "            reconstructed_state = reconstruct_state(measured_state, M)\n",
    "            \n",
    "            # Calculate TVD between true and reconstructed states\n",
    "            tvd = total_variation_distance(true_state, reconstructed_state)\n",
    "            tvd_values.append(tvd)\n",
    "        \n",
    "        # Store the average TVD for this shot count\n",
    "        results[noise_level][shots] = tvd_values\n",
    "\n",
    "# Calculate statistics\n",
    "mean_tvd = {noise: {shots: np.mean(results[noise][shots]) for shots in shots_range} \n",
    "            for noise in noise_levels}\n",
    "std_tvd = {noise: {shots: np.std(results[noise][shots]) for shots in shots_range} \n",
    "           for noise in noise_levels}\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12, 9))\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c']\n",
    "markers = ['o', 's', '^']\n",
    "\n",
    "for i, noise in enumerate(noise_levels):\n",
    "    means = [mean_tvd[noise][shots] for shots in shots_range]\n",
    "    stds = [std_tvd[noise][shots] for shots in shots_range]\n",
    "    \n",
    "    plt.errorbar(shots_range, means, yerr=stds, fmt=markers[i], \n",
    "                 capsize=5, label=f'Calibration Error: {noise}', \n",
    "                 color=colors[i], markersize=8, linewidth=2)\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Number of Measurement Shots', fontweight='bold')\n",
    "plt.ylabel('Total Variation Distance (TVD)', fontweight='bold')\n",
    "plt.title('Error in State Reconstruction vs. Measurement Statistics', \n",
    "          fontsize=14, fontweight='bold')\n",
    "plt.grid(True, which=\"both\", ls=\"--\", alpha=0.7)\n",
    "plt.legend(loc='upper right', frameon=True, fancybox=False, \n",
    "           edgecolor='black', framealpha=0.8)\n",
    "\n",
    "# Add annotations\n",
    "plt.text(0.02, 0.02, 'Lower TVD = Better reconstruction fidelity', \n",
    "         transform=plt.gca().transAxes, fontsize=10, style='italic')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot the distribution of TVD values for different shot counts at a fixed noise level\n",
    "fixed_noise = 0.05  # Choose a fixed noise level for detailed analysis\n",
    "plt.figure(figsize=(15, 9.75))\n",
    "\n",
    "for i, shots in enumerate(shots_range):\n",
    "    plt.subplot(2, 3, i+1)\n",
    "    sns.histplot(results[fixed_noise][shots], kde=True, \n",
    "                 color=colors[1], alpha=0.7, bins=15)\n",
    "    plt.title(f'{shots} Shots')\n",
    "    plt.xlabel('Total Variation Distance')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.axvline(mean_tvd[fixed_noise][shots], color='red', linestyle='--', \n",
    "                label=f'Mean: {mean_tvd[fixed_noise][shots]:.4f}')\n",
    "    plt.legend()\n",
    "\n",
    "plt.suptitle(f'Distribution of Reconstruction Errors (Calibration Error = {fixed_noise})', \n",
    "             fontsize=16, fontweight='bold')\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "plt.show()\n",
    "\n",
    "# Print numerical results\n",
    "print(\"Mean Total Variation Distance (TVD) values:\")\n",
    "for noise in noise_levels:\n",
    "    print(f\"\\nCalibration Error Level: {noise}\")\n",
    "    for shots in shots_range:\n",
    "        print(f\"  {shots} shots: {mean_tvd[noise][shots]:.6f} ± {std_tvd[noise][shots]:.6f}\")\n"
   ]
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